Many works have been devoted to query reformulating (QR) to correct misinterpreted user queries [1]}, [2]}, [3]}, [4]}, [5]}, [6]}. Among QR systems, the retrieval and ranking based approaches are efficient and widely-used in the industry [2]}, [1]}, [9]}. These works focus more on retrieval models without additional entity information. However, in voice assistant systems where task-oriented queries are usually short and ambiguous, the query intention may not be fully interpreted and this limitation negatively impact the performance of QR system whose relevance strongly relies on the query. For instance, a user's input query ("play long distance love by Sheena Easton") contains a correct singer name ("Sheena Easton") but also a lyric ("long distance love") instead of the correct song name ("telefone"). To reformulate this query so that virtual voice assistants can find the target song to play, the QR system needs to utilize the existing entities to obtain the correct song name "telefone"; besides, the model also needs to make a decision between two candidates ("play long distance love by little feat" and "play telefone by sheena easton") as they both contain key words ("long distance love" or "sheena easton") in the original query.
